Instructions to use ProbeX/Model-J__ResNet__model_idx_0183 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProbeX/Model-J__ResNet__model_idx_0183 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ProbeX/Model-J__ResNet__model_idx_0183") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0183") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0183", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0b927e8e9ff3c5b5c2147b60212becb34bdc82084cd1ba05ed5438e1bb877018
- Size of remote file:
- 171 MB
- SHA256:
- b3bb9afa86133f9db2f77aeb020bcd188ec6ec63bbe9c49f5eae5da479cf2b45
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